Communication Metrics Research Articles

Green Communication in IoT for Enabling Next-Generation Wireless Systems

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput.

Optimized intelligent 3D localization in wireless sensor networks for better data sharing

WSNs are networks of small, autonomous devices called sensors or nodes that are equipped with sensors, processing capabilities, and wireless communication capabilities. These networks are designed to monitor and collect data from their surrounding environments and transmit that data wirelessly to a central location for analysis. However, challenges such as packet loss and limited throughput have impeded their efficiency. To address these issues, this research presents an innovative approach known as Squirrel-based Elman Neural Localization (SbENL) to optimize 3D localization in WSNs. Squirrel fitness tracks the node's location in the sensor network environment. This study initially configures WSN nodes with constrained energy resources, and data rates are continually monitored and predicted. The outcome is improved data sharing with higher throughput rates. The research assesses the performance of SbENL against existing localization techniques, demonstrating its effectiveness in minimizing data loss, reducing energy consumption, and maximizing data transfer rates. This optimized intelligent 3D localization approach holds promise for enhancing data sharing and communication efficiency in WSNs, benefiting a wide range of applications. Finally, the communication metrics were measured and valued with recent existing approaches. A SbENL has minimized the data loss energy consumption and maximized the data transferring rate.

A jamming power control game with unknown user’s communication metric

We consider a jamming problem in which a jammer aims to degrade a user’s communication in which the user might differ in applied applications or communication purposes. Such differences are reflected by different communication metrics used by the user. Specifically, signal-to-interference-plus-noise ratio (SINR) is used as a metric to reflect regular data transmission purposes. Meanwhile, as another metric, latency, modeled by the inverse SINR, is used to reflect emergency communication purposes. We consider the most difficult scenario for the jammer where it does not know which application (metric) the user employs. The problem is formulated as a Bayesian game. Equilibrium is found in closed form, and the dependence of equilibrium on network parameters is illustrated.

Development of a routing method for ground-air Ad-Hoc network of special purpose

The object of the study is the process of forming control decisions to ensure the operation of the ground-air communication network routing subsystem based on neural network algorithms. The carried-out research is based on the application of the numerical-analytical approach to the selection of modern scientific and applied solutions for building management models for promising Ad-Hoc communication networks. In the Google Collab simulation environment, using the Python programming language, it was possible: firstly, to simulate the operation of a ground-to-air communication network based on previously obtained models and a routing process management system based on the FA-OSELM algorithm. Secondly, in accordance with the scenario of route construction and maintenance described in the article, to experimentally determine the communication metrics of the proposed method of intelligent routing of the ground-air Ad-Hoc special-purpose network, in order to assess its efficiency, adequacy and reliability of the results obtained. Thus, in order to evaluate the effectiveness of the proposed solutions, a comparative analysis of the application of three existing routing methods (FLCA, Q-Routing, Neuro Routing) used in Ad-Hoc networks relative to the developed method was conducted. The result of the experiment showed that the proposed routing method MAODV-FA-OSELM provides significant advantages over analogs. Thus, the method exhibits the best network throughput (2.12e+06), the lowest average network latency (0.12), the lowest packet loss (6.32), the lowest bit error rate (2.41), and the lowest overhead (0.10e+06). However, it should be noted that a promising direction of further research may be the study of the computational complexity of the routing management process and the determination of the minimum allowable representative sample of initial data to ensure online decision-making.

An End-to-End Network Evaluation Method for Differentiated Multi-Service Bearing in VPP

Virtual power plant (VPP) plays an important role in improving the balance and regulation abilities of new power system. The safe and reliable operation is support by the VPP end-to-end communication network with differentiated multi-service bearing capability. For the requirement of unified and standard VPP end-to-end networking scheme, the VPP service communication metrics, as well as the communication network architecture of VPP aggregation and control are analyzed. Then, a multi-dimension hierarchical VPP end-to-end network evaluation index system is put forward. In addition, an end-to-end VPP network evaluation method considering differentiated time-sensitive and granular requirements of multiple services is proposed. Finally, the suitability analysis results of various end-to-end networking schemes and multiple services with differentiated time-sensitive and granular requirements are given, which plays a guiding role in establishing a unified standard VPP end-to-end networking scheme.

Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation.

Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates alternate deep learning-based lightweight, knowledge-distilled, diarization solutions for segmenting classroom interactions of 3-5 years old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our lightest CNN model achieves a best F1-score of ∼76.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with automatic speech recognition-based re-segmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations.

Integrated Sensing and Communication-Assisted User State Refinement for OTFS Systems

Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.

DYNAMIC KEY GENERATION AND DISTRIBUTION COMPARISON USING MACHINE LEARNING INTEGRATED NODE AUTHENTICATION ROUTING PROTOCOL FOR IMPROVING QOS IN VANET

VANET, or Vehicular Adhoc Network, is widely used in mobile networks that use self-driving, navigation, entertainment, and other emergency applications. It is commonly adopted in the transport system to facilitate communication between the vehicles and crucial points. It is used to control traffic, improve routing efficiency, and better functioning of the transportation systems and controls. It enables communication between the mobile nodes through several base stations. The effectiveness of the VANET is improved through Artificial intelligence techniques. With the wide adoption of VANET networks for better communication, the network is subjected to various security vulnerabilities. This paper proposes a key generation distribution and comparison-based user authentication routing protocol to improve the security of the VANET. The communication metrics of the networks are monitored, and the data obtained are processed through the RNN algorithm. The nodes are authenticated using bilinear mapping, which involves registration, authentication, and verification. The nodes are identified during registration, a private key is assigned for temporary authentication, and the nodes are verified. NS3 is used to simulate the proposed model, where it obtained throughput, PDR, energy efficiency, and packet loss of 99.8, 99.3, 99, and 0.02%, respectively.

Narrow band of vehicular things communication system using hybrid pelican-beetle swarm optimization approach for intelligent transportation system

Congestion is caused by the continually expanding number of vehicles worldwide. Intelligent Transportation Systems (ITSs) could be used to build a system that efficiently uses the existing infrastructure and current technological advancements. Several Vehicular applications use the restricted vehicle battery in V2X systems, and a key concern is communication secrecy. Earlier research has focused on channel measurement, autonomous piloting, high-speed transmissions, secretive communications, and QoS analysis but battery-limited V2X has received far less attention. Therefore, resolving the abovementioned complications in the current model is imperative. Thus, a new V2X communication model is developed based on NB-IoT (NarrowBand Internet of Things) approaches. NB -IoT is utilized in the V2X communication model to offer low-cost, high-range, and minimal power to the communication environment. The objective of the proposed NarrowBand of Vehicular Things Communication (NBVTCS)System is to analyze the performance metrics of vehicular communication including energy efficiency rate with different SNR, bit error rate with different vehicular positions, and overall throughput of the system. The simulation was carried out to attain an effective energy efficiency rate, the system's transmit power, bit error rate, and overall system throughput. The proposed vehicular architecture also optimized the abovementioned vehicular metrics with the help of the proposed Hybrid Pelican Beetle Swarm Optimization (HPBSO) approach to achieve a higher accuracy in communication parameters. Finally, the simulation outcome demonstrates that the developed vehicular model is achieved with a minimal energy consumption rate and maximum throughput and also compares the proposed HPBSO technique with other optimization techniques including DOX, EFO, POA, and BSO.

Closed-Form Model for Performance Analysis of THz Joint Radar-Communication Systems

As an enabler technology in future network, terahertz (THz) joint radar-communication (T-JRC) has been envisioned as a solution to overcome the challenges in THz communications. Recent works on JRC mainly emphasize either performance tradeoffs or mutual benefits between radar and communication functionalities. Unable to characterize both benefit and tradeoff relationships, existing metrics and models are inapplicable to performance analysis on T-JRC systems. In this paper, a closed-form T-JRC performance model are proposed to explore the relationship between radar and communication metrics. To begin with, a novel T-JRC system model concerning random mobility and THz beam tracking is put forward. Next, the performance bounds of T-JRC performance in terms of communication rate and a proposed radar rate are acquired in three different integration cases. Furthermore, the expression of communication rate by radar rate in T-JRC is obtained theoretically, which is the first time to derive the closed-form relationship considering both benefits and tradeoffs to the best of our knowledge. Finally, numerical results validate the proposed model as well as closed-form expression, showing that the model succeeds in benefit and tradeoff relationship characterization in T-JRC systems and the fitting bounds are applicable in non-extreme scenarios.

Orthogonal Layers of Parallelism in Large-Scale Eigenvalue Computations

We address the communication overhead of distributed sparse matrix-(multiple)-vector multiplication in the context of large-scale eigensolvers, using filter diagonalization as an example. The basis of our study is a performance model, which includes a communication metric that is computed directly from the matrix sparsity pattern without running any code. The performance model quantifies to which extent scalability and parallel efficiency are lost due to communication overhead. To restore scalability, we identify two orthogonal layers of parallelism in the filter diagonalization technique. In the horizontal layer the rows of the sparse matrix are distributed across individual processes. In the vertical layer bundles of multiple vectors are distributed across separate process groups. An analysis in terms of the communication metric predicts that scalability can be restored if, and only if, one implements the two orthogonal layers of parallelism via different distributed vector layouts. Our theoretical analysis is corroborated by benchmarks for application matrices from quantum and solid state physics, road networks, and nonlinear programming. We finally demonstrate the benefits of using orthogonal layers of parallelism with two exemplary application cases—an exciton and a strongly correlated electron system—which incur either small or large communication overhead.

Robust beamforming design for UAV communications based on integrated sensing and communication

Integrated sensing and communication (ISAC) has been a promising technique in various wireless communication applications. In this paper, we investigate a beamforming design method based on ISAC waveforms in unmanned aerial vehicle (UAV) communications. An integrated state prediction and beamforming design framework is presented. We utilize the target states from sensing algorithms to improve the prediction performance. Based on the predicted states, we formulate the mathematical form of the communication interruption probability. To enhance the beamforming performance, we propose a design approach that satisfies both sensing and communication metrics and the communication interruption constraints. We show that the proposed method achieves robust communication under the integrated state prediction and beamforming design framework. Simulation results show that by using the ISAC signal, our method significantly lowers the communication interruption probability in the beamforming process and achieves better communication performance.

Evaluating Nontechnical Skills and Leadership Skills During Simulated Critical Care Scenarios

Nontechnical skills (NTS) of healthcare teams are fundamental to successful and safe surgeries. This study aims to identify the communication measures associated with overall NTS and leadership skills. Ten medical students assuming the roles of attending physicians completed two simulated critical care scenarios containing five disruptive integrated events. Half of the students received standardized NTS leadership training between scenarios, while the other half served as an active control. The group of participants that received the leadership training had a statistically higher difference in NTS score by 0.77 than the control group (p = 0.05). When investigating which communication metrics best predicted NTS scores, strong negative correlations corresponding to Call-outs from Nurses to Physicians (ρ = -0.59) during scenarios and Requests from Nurses to physicians (ρ = -0.52) during events were found. Overall, decomposing communication from participants to team members can provide insight into leadership measurement and training for care providers.

Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks

The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.

Doubly Constrained Waveform Optimization for Integrated Sensing and Communications

This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. This provides significant flexibility in meeting system performance. We formulate three different optimization problems that constrain the radar performance only, the communication performance only, and the ISAC performance, respectively. New techniques are developed to solve the original problems, which are NP-hard and cannot be directly solved by conventional semi-definite programming (SDP) techniques. Novel gradient descent methods are developed to solve the first two problems. For the third non-convex optimization problem, we transform it into a convex problem and solve it via convex toolboxes. We also disclose the connections between three optimizations using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions.

Performance analysis of vehicle-to-everything communication using internet of LoRa computing for intelligent transportation system

Intelligent Transportation System (ITS) is an emerging field nowadays that is widely utilized to improve safety measures, avoid abnormalities, and traffic flow control, and also develops the environment without hassle. So far, the deployment of sensors into vehicles and the analyzing the vehicular parameters towards the smart city applications have been achieved by the integration of LoRa-based vehicular communication. However, trust in previous design architecture should need efficient transmission, robustness, and energy efficiency. To overcome the challenges, the proposed system designed the Internet of LoRa computing enabled vehicular communication with high reliability by offering the optimization technique namely an Enhanced Artificial Bee Colony (EABC) algorithm for the localization scheme. The proposed framework consists of two sections. First, observe the objects nearby vehicles using an ultrasonic sensor that is equipped in the Arduino module with a LoRa shield. The second work contributes to the evaluation of performance metrics of vehicular communication in the sensing region with a minimum delay of two seconds using MathWorks simulation. The article designed the VANET, which utilized the LoRa architecture for Vehicle to Everything communication, and pointed out the position of the sensor nodes using a localization scheme (EABC algorithm), comparing the proposed EABC algorithm with the other optimization techniques viz Particle Swarm Optimization and Genetic algorithms in the dense nodes and it achieves 25% variation in minimizing the position error at a certain speed. Further, find the system performance by calculating the BER (Bit Error Rate) in both coherent and non-coherent with varying speeds of the vehicle and router connections and it achieves 40% variation in efficiency and realizes the network coverage in terms of the position of the vehicle in the way the proposed framework achieves the high accuracy in overall system throughput.

Control-Oriented Power Allocation for Integrated Satellite-UAV Networks

This letter presents a sensing-communication-computing-control (SC3) integrated satellite unmanned aerial vehicle (UAV) network, where UAVs are equipped with sensors, mobile edge computing (MEC) servers, base stations and satellite communication modules. Like a nervous system, this integrated network is capable of organizing multiple field robots in remote areas, so as to perform mission-critical tasks which are dangerous for humans. Aiming at activating this nervous system with multiple SC3 loops, we present a control-oriented optimization problem. Different from traditional studies which mainly focused on communication metrics, we address the power allocation issue to minimize the sum linear quadratic regulator (LQR) control cost of all SC3 loops. Specifically, we show the convexity of the problem and reveal the relationship between the optimal transmit power and intrinsic entropy rates of different SC3 loops. For the assure-to-be-stable case, we derive a closed-form solution for ease of practical applications. After demonstrating the superiority of the control-oriented power allocation, we further highlight its difference from the classic capacity-oriented water-filling method.

ESCALB: An effective slave controller allocation-based load balancing scheme for multi-domain SDN-enabled-IoT networks

In software-defined networking (SDN), several controllers improve the reliability as well as the scalability of networks such as the Internet-of-Things (IoT), with the distributed control plan. To achieve optimal results in IoT networks, an SDN can be employed to reduce the complexity associated with IoT and provide an improved quality-of-service (QoS). With time, it is likely expected that the demand for IoT will rise, and a large number of sensors will be connected, which can generate huge network traffic. With these possibilities, the SDN controllers processing capacity will be surpassed by the traffic sent by the IoT sensors. To handle this kind of challenge, and achieve promising results, a dynamic slave controller allocation with a premeditated mechanism can play a pivotal role to accomplish the task management and migration plan. Following this, we proposed an efficient slave controller allocation-based load balancing approach for a multi-domain SDN-enabled IoT network, which aims to transfer switches to a controller with idle resources effectively. Among several slave controllers for selecting a target controller, a multi-criteria decision-making (MCDM) strategy, i.e., an analytical network process (ANP) has been used in our approach to enrich communication metrics and maintain high-standard QoS statistics. Moreover, switch migration is modeled with knapsack 0/1 problem to achieve maximum utilization of the slave controllers. Our proposed scheme enabled with a flexible decision-making process for selecting controllers with varying resources. The results demonstrated with emulation environment show the effectiveness of the ESCALB.

A Lightweight Blockchain-Based Remote Mutual Authentication for AI-Empowered IoT Sustainable Computing Systems

Internet of Things (IoT) has led to significant advancements in communication technologies, specifically, concerning IoT Based Sustainable Information Systems. Lately, Industry-Academic communities have made great strides for the development of security in IoT-based applications, such as traffic management, industrial automation systems, military surveillance systems, transportation, parking, etc. The sustainable IoT converges AI and blockchain technologies for enhancing quality of individual’s life. As a result, emerging IoT applications operate a distributed ledger technology to provide robust-level of encryption and execution for contractual agreement that resolves interoperability and security issues. Thus, this paper proposes a blockchain-based remote mutual authentication (B-RMA) that considers smart devices and cloud networks to offer security and privacy. The proposed B-RMA can coexist with IoT based smart environment to decentralize the processing of user authentication requests. The prominence of the proposed strategies including security efficiency and privacy protection is evaluated using informal security analysis. Moreover, a run-time platform "Node.js" was used to analyze the communication metrics such as execution time, throughput, and overhead ratio over the concurrent requests. The investigation results prove that the B-RMA achieves a scalable environment, accordingly.

Evaluating the Factors Affecting Scholarly Communication of Journal Articles on Social and News Media: An Altmetric Study

Different factors influence the altmetric attentions for scholarly articles on social media. This study aimed to evaluate the effecting factors on altmetric coverage of journals in non-English-speaking countries. Using a total population sampling technique, we included all Iranian and Turkish journals published from 2016 to 2019. Altmetric data were collected from altmetric.com using an application programming interface, for the coverage of mentions aggregated by the journal articles on Twitter, Facebook, and news media. The correlations between the languages, field of study, Google PageRank (GPR) score, and availability of a ‘share button’ with mentions were calculated using non-parametric tests. Among all articles, 2,378 articles were scholarly communicated on social media, and there were 7,191 mentions in the evaluated platforms. The scholarly publication of Iran and Turkey differed greatly concerning the subject matter. However, Twitter ranked first among the highly used alternative metrics for scholarly communication in both countries. The number of mentions for English journals was higher than for bilingual ones. Moreover, there was a positive correlation between the GPR and the coverage of mentions on Twitter and news media.